Z probabilistic linguistic term sets and its application in multi-attribute group decision making

Chai, Jiahui; Xian, Sidong; Lu, Sichong

doi:10.1007/s10700-021-09351-2

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…Representation Structure [61,73] Pythagorean linguistic preference relations [63] Flexible Linguistic Expressions [64] Double hierarchy linguistic preference relations [74] Comparative linguistic expressions [65][66][67] Z-numbers and their extensions [68] Nonlinear preference relations [60] Self-confident linguistic preference relations [70,71] q-rung orthopair fuzzy preference relations [72] Complex intuitionistic fuzzy preference relations [69,75,76] Probabilistic linguistic preference relations [77] Heterogeneous preference relations erence loss is then proposed for FLEs to construct the collective evaluation and the feedback mechanism benefits from consensus rules with minimum preference loss to adapt inconsistent opinions. More recently, a preference structure is proposed in [64] based upon augmenting the concepts of self-confidence degree and double hierarchy linguistic preference relation (DHLPR).…”

Section: Referencementioning

confidence: 99%

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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Section: Referencementioning

confidence: 99%

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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“…They have different skill-sets and experiences. The PLTS does not factor this and therefore the PLTS was combined with the concept of Z-Numbers by Chai et al [108] in 2021, leading to the Z-Probabilistic Linguistic Term Set (ZPLTS). The concept of Z-Numbers has been around since 2011, when it was proposed by Zadeh [109] as a way of assigning a credibility value to an evaluation value.…”

Section: State-of-the-art Of Electrementioning

confidence: 99%

“…This can lead to inaccurate results in the decision-making process. To overcome this, the concepts of Z-Numbers and PLTSs were combined to create the Z-Probabilistic Linguistic Term Set (ZPLTS) [108]. A ZPLTS can be defined in Eq.…”

Section: Z-probabilistic Linguistic Term Setmentioning

confidence: 99%

“…In this study the concept of ZPLTS is combined with ELECTRE II to create a novel algorithm, the ZPLTS-ELECTRE II. The authors in [108] proposed a technique for normalizing ZPLTSs, as well as some methods for comparing ZPLTSs, specifically the concepts of score, deviation degree, and distance. These concepts are elaborated on as follows.…”

Section: Z-probabilistic Linguistic Term Setmentioning

confidence: 99%

“…In order to make use of ZPLVs they need to be comparable with each other. Chai et al [108] proposed the score method to compare two ZPLVs. Larger scores correlate with larger ZPLVs, allowing for their comparison.…”

Section: Score Of Zplvsmentioning

confidence: 99%

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Application of ELECTRE algorithms in ontology selection.

Sooklall

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The field of artificial intelligence (AI) is expanding at a rapid pace. Ontology and the field of ontological engineering is an invaluable component of AI, as it provides AI the ability to capture and express complex knowledge and data in a form that encourages computation, inference, reasoning, and dissemination. Accordingly, the research and applications of ontology is becoming increasingly widespread in recent years. However, due to the complexity involved with ontological engineering, it is encouraged that users reuse existing ontologies as opposed to creating ontologies de novo. This in itself has a huge disadvantage as the task of selecting appropriate ontologies for reuse is complex as engineers and users may find it difficult to analyse and comprehend ontologies. It is therefore crucial that techniques and methods be developed in order to reduce the complexity of ontology selection for reuse. Essentially, ontology selection is a Multi-Criteria Decision-Making (MCDM) problem, as there are multiple ontologies to choose from whilst considering multiple criteria. However, there has been little usage of MCDM methods in solving the problem of selecting ontologies for reuse. Therefore, in order to tackle this problem, this study looks to a prominent branch of MCDM, known as the ELimination Et. Choix Traduisant la RÉalite (ELECTRE). ELECTRE is a family of decision-making algorithms that model and provide decision support for complex decisions comprising many alternatives with many characteristics or attributes. The ELECTRE algorithms are extremely powerful and they have been applied successfully in a myriad of domains, however, they have only been studied to a minimal degree with regards to ontology ranking and selection. In this study the ELECTRE algorithms were applied to aid in the selection of ontologies for reuse, particularly, three applications of ELECTRE were studied. The first application focused on ranking ontologies according to their complexity metrics. The ELECTRE I, II, III, and IV models were applied to rank a dataset of 200 ontologies from the BioPortal Repository, with 13 complexity metrics used as attributes. Secondly, the ELECTRE Tri model was applied to classify the 200 ontologies into three classes according to their complexity metrics. A preference-disaggregation approach was taken, and a genetic algorithm was designed to infer the thresholds and parameters for the ELECTRE Tri model. In the third application a novel ELECTRE model was developed, named ZPLTS-ELECTRE II, where the concept of Z-Probabilistic Linguistic Term Set (ZPLTS) was combined with the traditional ELECTRE II algorithm. The ZPLTS-ELECTRE II model enables multiple decision-makers to evaluate ontologies (group decision-making), as well as the ability to use natural language to provide their evaluations. The model was applied to rank 9 ontologies according to five complexity metrics and five qualitative usability metrics. The results of all three applications were analysed, compared, and contrasted, in order to understand the applicability and effectiveness of the ELECTRE algorithms for the task of selecting ontologies for reuse. These results constitute interesting perspectives and insights for the selection and reuse of ontologies.

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A Multi-criteria Group Decision Making Based on Possibility Degree Matrix and $$\mathbb {ZPDHL}$$-VIKOR Method

et al. 2023

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Z probabilistic linguistic term sets and its application in multi-attribute group decision making

Cited by 16 publications

References 23 publications

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Application of ELECTRE algorithms in ontology selection.

A Multi-criteria Group Decision Making Based on Possibility Degree Matrix and $$\mathbb {ZPDHL}$$-VIKOR Method

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